IEEE Transactions on Radar Systems最新文献

{"title":"Sensing-Aided Distortion Estimation for OFDM Radar With Nonlinear Transmitter","authors":"Seonghyeon Kang;Kawon Han;Songcheol Hong","doi":"10.1109/TRS.2024.3452868","DOIUrl":"https://doi.org/10.1109/TRS.2024.3452868","url":null,"abstract":"This article presents a method to estimate nonlinear distortion of an orthogonal frequency-division multiplexing (OFDM) radar signal by using detected target parameters. Since high transmitting power is desirable for OFDM radar to have a long detection range, the transmitter (TX) is preferred to work in a nonlinear region for high power efficiency. This causes strong distortions of the OFDM radar signals, which have a high peak-to-average power ratio (PAPR). Conventionally, this distortion can be compensated by utilizing equalization at the receiver (RX) or digital predistortion (DPD) at the TX. However, both approaches require information on the transmitted signals obtained through an additional feedback path, which increases the hardware complexity of the radar system. To address this issue, a sensing-aided distortion estimation (SADE) is proposed to estimate the distorted OFDM signals. Initially, radar processing is performed on the received signals with the prior known undistorted symbols. This allows detection of some initial targets in the range-Doppler (RD) domain. Once the target parameters are detected, the distorted symbols can be estimated through division of the received signals by the calculated target signals. This approach leverages the initial target sensing as a feedback loop between the TX and RX. This allows estimation of the distorted OFDM symbols without any additional hardware. The radar processing for subsequent targets demodulates the received signals by using the estimated distorted symbols.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"821-831"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Dimensional Group Target Separation Detection Method Based on Ellipsoid Shape Reconstruction","authors":"Zhennan Liang;Zihan Yan;Meng Gao;Shaoqiang Chang;Quanhua Liu","doi":"10.1109/TRS.2024.3449347","DOIUrl":"https://doi.org/10.1109/TRS.2024.3449347","url":null,"abstract":"An important challenge in group target tracking is the separation of individual targets within the group. Group target separation can lead to significant fluctuations in the position of the group center and the shape of the group, leading to decreased tracking accuracy and potential target loss. Moreover, when the target group consists of multiple objects with uncertain spatial positions, especially during separation, rapidly reconstructing the shape of the group target becomes challenging. This article proposes a novel method for detecting separation events and stable tracking to address related issues. Initially, we establish rapid ellipsoidal modeling of the group target shape through measurement mapping. Subsequently, group target separation events are predicted in real time by monitoring changes in ellipsoid volume between frames. Meanwhile, an adaptive association gate and a group clustering threshold are set to assist in separation assessment. In addition, utilize the pre-separation group target state to stabilize subgroups’ tracking after separation. The simulation results demonstrate that the proposed algorithm effectively and timely detects group target separation and enhances the performance of tracking separated group targets.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"767-777"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Over-the-Air Synchronization for Coherent Digital Automotive Radar Networks","authors":"Lukas Sigg;Lucas Giroto de Oliveira;Zsolt Kollár;Jan Schöpfel;Tobias T. Braun;Nils Pohl;Thomas Zwick;Benjamin Nuss","doi":"10.1109/TRS.2024.3449333","DOIUrl":"https://doi.org/10.1109/TRS.2024.3449333","url":null,"abstract":"Radar networks can offer superior performance compared to individual sensors. However, synchronization is crucial for realizing such a radar network coherently. Digital systems, in particular, provide new opportunities for over-the-air synchronization via signal processing. To synchronize the nodes of a digital radar network, correction of the carrier frequency offset (CFO), sampling frequency offset (SFO), and timing offset (TO) is necessary. A coarse synchronization can be achieved, for example, afterward through low-frequency (LF) coupling of the individual sensors, with fine synchronization realized through signal processing. For fine synchronization, either a target or coupling between the two radar sensors with sufficient signal-to-noise ratio (SNR) is required. The limits of this synchronization approach are primarily defined by the range and Doppler shift ambiguities of the individual sensors. In this article, simulations and measurements demonstrate the feasibility of such a system.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"739-751"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phased Array Weather Radar Architectures for Doppler Estimation With Space-Time Processing","authors":"Yoon-SL Kim;David Schvartzman;Robert D. Palmer;Tian-You Yu;Feng Nai;Christopher D. Curtis","doi":"10.1109/TRS.2024.3444785","DOIUrl":"https://doi.org/10.1109/TRS.2024.3444785","url":null,"abstract":"Polarimetric weather radars, such as the Weather Surveillance Radar-1988 Doppler (WSR-88D), improve weather forecasts and provide valuable data for operational and scientific applications. The polarimetric capability adds additional insight into storm microphysics and greatly improves precipitation estimates. Nevertheless, fast-evolving weather events require high-temporal resolution data, which conventional radar systems (mechanical and dish-based) cannot provide. Phased array radar (PAR) offers superior observation capabilities with electronic beam steering and enhanced scanning agility. Furthermore, digital PAR enables 1-D (space and time) processing and overcomes limitations in clutter mitigation compared with the traditional radar systems that only use Doppler processing. Doppler processing is traditionally used to effectively filtering out ground clutter with zero mean velocity. In contrast, space-time processing (STP) enhances clutter mitigation to filter out both stationary and moving clutter through the joint spatial and temporal spectrum. This study aims to apply STP (nonadaptive) and space-time adaptive processing (STAP) to weather radar data and explore their benefits to improve Doppler velocity estimation of meteorological returns. Furthermore, the performance of STP and STAP for different digital PAR back ends, including fully digital and subarray systems, is investigated. Preliminary findings underscore the critical role of radar scanning parameters and environmental conditions, such as sample quantity, clutter-to-signal ratio (CSR), and signal-to-noise ratio (SNR), in the Doppler velocity estimation. Data collected with the recently completed Horus radar system are evaluated using STP and STAP. Results demonstrate the potential for improving data quality, particularly in Doppler velocity estimation within cluttered environments, through the application of STP and STAP techniques. The filtering algorithm with STAP demonstrates a substantial reduction in error within the Doppler velocity estimation, achieving approximately an eightfold improvement compared with the estimation derived from STP with filtering.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"725-738"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beampattern Shaping in 4-D Imaging Automotive MIMO Radars","authors":"Masoud Dorvash;Mahmoud Modarres-Hashemi;Mohammad Alaee-Kerahroodi","doi":"10.1109/TRS.2024.3443301","DOIUrl":"https://doi.org/10.1109/TRS.2024.3443301","url":null,"abstract":"This article presents a method for designing transmit beampattern in 4-D imaging automotive multiple-input-multiple-output (MIMO) radars, employing the distance between the designed and desired beampatterns as the design metric. Utilizing the \u0000<inline-formula> <tex-math>$ell _{p}$ </tex-math></inline-formula>\u0000-norm criteria, we consider a broader range of p values, specifically for \u0000<inline-formula> <tex-math>$p geq 2$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$0 lt p leq 1$ </tex-math></inline-formula>\u0000, to enhance the optimization framework. The optimization problem formulated under these criteria is efficiently solved using the block successive upper bound minimization (BSUM) technique for discrete and continuous phase constraints. Our analysis verifies the convergence of the objective function and confirms the solution’s convergence, thereby establishing a new stopping criterion for this optimization process. Furthermore, we demonstrate that our proposed method outperforms the commonly used omnidirectional beampattern across various automotive scenarios, highlighting its superior adaptability and utility in multiple applications. In addition, our methods demonstrate good performance and computational efficiency, making them suitable for real-time 4-D imaging automotive BSUM radar applications.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"752-766"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142159102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Beamforming for Situation-Aware Automotive Radars Under Uncertain Side Information","authors":"Edoardo Focante;Nitin Jonathan Myers;Geethu Joseph;Ashish Pandharipande","doi":"10.1109/TRS.2024.3442388","DOIUrl":"https://doi.org/10.1109/TRS.2024.3442388","url":null,"abstract":"Radar is an important sensing modality that supports advanced levels of assisted and autonomous driving. In this work, we exploit side information, such as lane topology maps of the environment, position, and orientation information of the ego vehicle, to design beamformers in automotive radars. Specifically, we present a convex optimization-based method for transmit beamformer design using location-based static environment maps derived from georeferenced maps. The designed beams allocate less power along the directions where a static obstacle in the environment is closer and vice versa. We study the robustness of our situation-aware transmit beamforming technique to uncertainties in the position and orientation information of the ego vehicle. We also address these uncertainties by extending our situation-aware beamforming approach using tools from stochastic optimization (SO). Through simulations on the public dataset nuScenes, we show that our method achieves better detection than situation-agnostic radar sensing. Furthermore, our design is robust against errors in estimating the position and the orientation of the ego vehicle.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"699-711"},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurally Augmented Deep Unfolding for Automotive Radar Interference Mitigation","authors":"Jeroen Overdevest;Arie G. C. Koppelaar;Jihwan Youn;Xinyi Wei;Ruud J. G. van Sloun","doi":"10.1109/TRS.2024.3442692","DOIUrl":"https://doi.org/10.1109/TRS.2024.3442692","url":null,"abstract":"The proliferation of active radar sensors deployed in vehicles has increased the need for mitigating automotive radar-to-radar interference. While simple avoidance and mitigation methods are still effective today, the expected crowded spectrum allocations pose new challenges that likely require more sophisticated techniques. In particular, interference mitigation methods that can handle significant levels of radar signal corruption are required. To this end, we propose neurally augmented analytically learned fast iterative shrinkage thresholding algorithm (NA-ALFISTA), which is a neural network-based solution for reconstructing time-domain radar signals by leveraging sparsity in the range-Doppler map (RDM). The neural augmentation network is deployed as a single gated recurrent unit (GRU) cell that captures the radar signal statistics along the unfolded layers of fast-iterative shrinkage thresholding algorithm (FISTA)-based sparse recovery, which significantly boosts the convergence rate. It estimates the next layer’s parameters necessary in ALFISTA based on the previous layer’s output. The proposed method is compared to state-of-the-art detect-and-repair methods and source separation methods in simulated data and real-world measurements.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"712-724"},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Data Storage Method for Pulse-to-Pulse Optimizations","authors":"Trevor Van Hoosier;Jordan Alexander;Mariah Montgomery;Austin Egbert;Justin Roessler;Charles Baylis;Robert J. Marks","doi":"10.1109/TRS.2024.3428450","DOIUrl":"https://doi.org/10.1109/TRS.2024.3428450","url":null,"abstract":"Due to increasing congestion in the radar frequencies due to reallocations, the pressure upon radar systems to avoid interference through dynamically changing operating frequency has intensified. Many modern radar systems (often called “cognitive radar” systems) often have the ability to sense and avoid interference. Through the use of reconfigurable transmitter circuitry, the front end can be quickly reconfigured following a change in frequency to maximize output power and, hence, detection range. With the implementation of a fast, plasma-switch impedance tuner paired with an efficient circuit optimization, the ability to change tuner setting within a single radar pulse repetition interval (PRI) has been previously demonstrated. To carry out impedance-tuning optimization measurements for each PRI, an efficient data storage and lookup method is needed. In this article, we demonstrate how hybrid storage with a hash table can be used with an efficient, cache replacement algorithm on a software-defined radio (SDR) platform to enable continuous operation with pulse-to-pulse optimization. This data storage approach minimizes overhead in storage of circuit optimization settings, allowing faster optimization of the circuit to maximize output power. By maximizing output power quickly, it is expected that the radar will experience better signal-to-interference-plus-noise ratio and accurate detection of targets at greater ranges.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"899-909"},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Radar-Communication Systems by Optimizing Radar Performance and Quality of Service for Communication Users","authors":"Christos G. Tsinos;Aakash Arora;Theodoros A. Tsiftsis","doi":"10.1109/TRS.2024.3425275","DOIUrl":"https://doi.org/10.1109/TRS.2024.3425275","url":null,"abstract":"In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization–minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"778-790"},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully Polarimetric Automotive Radar: Proof of Concept","authors":"Alessandro Tinti;Simon Tejero Alfageme;Sergi Duque Biarge;Jordi Balcells-Ventura;Nils Pohl","doi":"10.1109/TRS.2024.3423631","DOIUrl":"https://doi.org/10.1109/TRS.2024.3423631","url":null,"abstract":"The last few years suggest the rising interest of both, academia and industry, toward the application of polarimetry in the automotive radar world. The perspective of a more accurate comprehension of the surrounding environment through the use of orthogonal polarizations has now become very attractive, given the rising number of antennas available to automotive radar technology. This article aims to present a fully polarimetric automotive radar front end. The requirements of a polarimetric automotive radar are investigated and the design of a \u0000<inline-formula> <tex-math>$12 times 16$ </tex-math></inline-formula>\u0000 antenna system, working in the band 76–81 GHz, and fulfilling them is presented. The system calibration was carried out using a dihedral corner reflector. Its characteristics were analyzed in detail and exploited to reach an optimal alignment with the radar, thus allowing the polarimetric calibration through the measurement of only one target and one scattering matrix. The validity of the system and the potential impact of polarimetry on automotive radar applications are verified and presented through several real-radar measurements, both in a controlled environment in the anechoic chamber and outdoors. Different applications are investigated, such as multipath detection and target classification, by applying the Pauli decomposition to the polarimetric data.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"645-660"},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}